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MLOP: MATLAB Based Optimization Techniques

This one-day course introduces applied optimization in the MATLAB^® environment, focusing on using Optimization Toolbox and Global Optimization Toolbox. Topics include:

Defining the problem
Writing objective functions
Defining constraints
Choosing solvers and setting options
Using global optimization methods

Note: A 1 hour test session will be scheduled one day prior to the first day of class. This session is to verify that the visual and audio connection is working properly on your computer. The required product software should be installed for the test session. It is highly recommended that you attend this session to ensure a successful and timely class start.

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	Detailed course outline

Day 1 of 1
Optimization Fundamentals	Objective: Understand the basic structure and process of solving optimization problems effectively. Attendees use a hands-on example that introduces terminology and fundamental concepts, with a focus on realizing optimization in the MATLAB environment. Optimization Example: Designing a soup can Mathematical problem formulation Visual illustration of the problem Running an optimization using the Optimization Tool Interpreting the results
Writing Objective Functions	Objective: Mathematically express the quantity to be optimized in MATLAB. Pros and cons of various implementations are highlighted. The objective function interface Coding guidelines Objective functions as input Function handle data type Handles to function files Anonymous functions
Expressing Constraints	Objective: Add constraints to an optimization problem in MATLAB. Different types of constraints are considered, as well as guidelines for efficient implementation. Types of constraints Defining linear constraints Bounds and general linear inequalities Linear equations Defining nonlinear constraints Constraint function interface Coding guidelines
Selecting Solvers and Options	Objective: Select the most appropriate algorithm for a given problem by considering the different solvers and their associated options available in Optimization Toolbox. Algorithm background Choosing the toolbox function Optimization parameters and options Command line functionality Understanding the output
Global Optimization	Objective: Understand the extra solution methods available in Global Optimization Toolbox and how to work on optimization problems with features that cause classical algorithms to fail or work inefficiently. Limits of the Optimization Toolbox algorithms Introduction to algorithms in Global Optimization Toolbox Example: Global optimization Example: Shift scheduling Genetic algorithms in depth Interpretation of results

Prerequisites

MATLAB Fundamentals^®. Knowledge of linear algebra and multivariate calculus is helpful.

Course Length - 1 day